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Recent research demonstrate that prediction of time series by predictiverecurrent neural networks based on the noisy input generates a smoothanticipated trajectory. We examine influence of the noise component in both thetraining data sets and the input sequences on network prediction quality. Wepropose and discuss an explanation of the observed noise compression in thepredictive process. We also discuss importance of this property of recurrentnetworks in the neuroscience context for the evolution of living organisms.

It's a super deal -- train recurrent network on noisy data and get smooth prediction free